Biomedical subjects

Roel G W Verhaak

Publications and source records attributed to Roel G W Verhaak.

8 recordsLinked to original sources

PubMed2025

Spatial-Temporal Diversity of Extrachromosomal DNA Shapes Urothelial Carcinoma Evolution and Tumor-Immune Microenvironment.

Extrachromosomal DNA (ecDNA) presents a promising target for cancer therapy; however, its spatial-temporal diversity and influence on tumor evolution and the immune microenvironment remain largely unclear. We apply computational methods to analyze ecDNA from whole-genome sequencing data of 595 urothelial carcinoma (UC) patients. We demonstrate that ecDNA drives clonal evolution through structural rearrangements during malignant transformation and recurrence of UC. This supports a model wherein tumors evolve via the selective expansion of ecDNA-bearing cells. Through multi-regional sampling of tumors, we demonstrate that ecDNA contributes to the evolution of multifocality and increased intratumoral heterogeneity. EcDNA is present in 36% of UC tumors and correlates with an immunosuppressive phenotype and poor prognosis. Single-cell RNA sequencing analyses reveal that ecDNA+ malignant cells exhibit diminished expression of major histocompatibility complex class I molecules, enabling them to evade T-cell immunity. Finally, we show that sequencing of urinary sediment-derived DNA has excellent specificity in detecting ecDNA.

Journal Article↗

PubMed2023

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Humans↗

PubMed2006

Essential role of Jun family transcription factors in PU.1 knockdown-induced leukemic stem cells.

Knockdown of the transcription factor PU.1 (encoded by Sfpi1) leads to acute myeloid leukemia (AML) in mice. We examined the transcriptome of preleukemic hematopoietic stem cells (HSCs) in which PU.1 was knocked down (referred to as 'PU.1-knockdown HSCs') to identify transcriptional changes preceding malignant transformation. Transcription factors c-Jun and JunB were among the top-downregulated targets. Restoration of c-Jun expression in preleukemic cells rescued the PU.1 knockdown-initiated myelomonocytic differentiation block. Lentiviral restoration of JunB at the leukemic stage led to loss of leukemic self-renewal capacity and prevented leukemia in NOD-SCID mice into which leukemic PU.1-knockdown cells were transplanted. Examination of human individuals with AML confirmed the correlation between PU.1 and JunB downregulation. These results delineate a transcriptional pattern that precedes leukemic transformation in PU.1-knockdown HSCs and demonstrate that decreased levels of c-Jun and JunB contribute to the development of PU.1 knockdown-induced AML by blocking differentiation and increasing self-renewal. Therefore, examination of disturbed gene expression in HSCs can identify genes whose dysregulation is essential for leukemic stem cell function and that are targets for therapeutic interventions.

Animals↗

PubMed2006

HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics.

BACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. RESULTS: We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/. CONCLUSION: The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis.

Antigens, CD34↗

PubMed2006

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.

BACKGROUND: Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers. RESULTS: Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing. CONCLUSION: Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.

Algorithms↗

PubMed2006

Significance of murine retroviral mutagenesis for identification of disease genes in human acute myeloid leukemia.

Retroviral insertion mutagenesis is considered a powerful tool to identify cancer genes in mice, but its significance for human cancer has remained elusive. Moreover, it has recently been debated whether common virus integrations are always a hallmark of tumor cells and contribute to the oncogenic process. Acute myeloid leukemia (AML) is a heterogeneous disease with a variable response to treatment. Recurrent cytogenetic defects and acquired mutations in regulatory genes are associated with AML subtypes and prognosis. Recently, gene expression profiling (GEP) has been applied to further risk stratify AML. Here, we show that mouse leukemia genes identified by retroviral insertion mutagenesis are more frequently differentially expressed in distinct subclasses of adult and pediatric AML than randomly selected genes or genes located more distantly from a virus integration site. The candidate proto-oncogenes showing discriminative expression in primary AML could be placed in regulatory networks mainly involved in signal transduction and transcriptional control. Our data support the validity of retroviral insertion mutagenesis in mice for human disease and indicate that combining these murine screens for potential proto-oncogenes with GEP in human AML may help to identify critical disease genes and novel pathogenetic networks in leukemia.

Acute Disease↗

PubMed2005

Mutations in nucleophosmin (NPM1) in acute myeloid leukemia (AML): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance.

Mutations in nucleophosmin NPM1 are the most frequent acquired molecular abnormalities in acute myeloid leukemia (AML). We determined the NPM1 mutation status in a clinically and molecularly well-characterized patient cohort of 275 patients with newly diagnosed AML by denaturing high-performance liquid chromatography (dHPLC). We show that NPM1 mutations are significantly underrepresented in patients younger than 35 years. NPM1 mutations positively correlate with AML with high white blood cell counts, normal karyotypes, and fms-like tyrosine kinase-3 gene (FLT3) internal tandem duplication (ITD) mutations. NPM1 mutations associate inversely with the occurrence of CCAAT/enhancer-binding protein-alpha (CEBPA) and NRAS mutations. With respect to gene expression profiling, we show that AML cases with an NPM1 mutation cluster in specific subtypes of AML with previously established gene expression signatures, are highly associated with a homeobox gene-specific expression signature, and can be predicted with high accuracy. We demonstrate that patients with intermediate cytogenetic risk AML without FLT3 ITD mutations but with NPM1 mutations have a significantly better overall survival (OS) and event-free survival (EFS) than those without NPM1 mutations. Finally, in multivariable analysis NPM1 mutations express independent favorable prognostic value with regard to OS, EFS, and disease-free survival (DFS).

Acute Disease↗

PubMed2004

Prognostically useful gene-expression profiles in acute myeloid leukemia.

BACKGROUND: In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. METHODS: We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. RESULTS: Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. CONCLUSIONS: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.

Acute Disease↗